Platform Overview

The Complete Computer Vision Platform

From raw data to deployed models—IRIS provides everything you need to build, train, and operate computer vision systems at scale.

Explore Workflow

How IRIS Works

A continuous cycle of improvement—from raw data to production-ready models and back again

ContinuousIteration

Deploy improvements back into production

Connect

Visual Data Ingestion

IRIS connects to video streams, image repositories, and external data pipelines to centralize visual data ingestion. The platform normalizes incoming data and prepares it for scalable analysis workflows.

Core Capabilities

Everything you need to build production-grade computer vision systems

Visual Data Labeling

Structured dataset creation for computer vision systems

IRIS provides intuitive annotation tools for defining objects of interest and creating high-quality training datasets. Bounding regions, semantic labels, and metadata enable consistent dataset construction and reliable model evaluation.

Similarity-Based Discovery

Accelerated dataset expansion through visual pattern discovery

IRIS surfaces visually similar objects across datasets to accelerate dataset growth and uncover previously unseen examples. This discovery capability significantly reduces manual annotation effort while improving dataset coverage.

Architecture-Agnostic Training

Experiment across multiple detection approaches

IRIS enables structured experimentation across multiple model architectures within a unified training environment. Performance metrics, training runs, and experiment comparisons are tracked to support informed model selection.

Experiment Management

Full lifecycle experiment tracking

IRIS tracks training runs, evaluation metrics, and dataset versions to ensure reproducible experimentation. Teams can compare performance across experiments and maintain full visibility into model evolution.

Flexible Deployment

Deploy models to operational environments

IRIS supports deployment across cloud infrastructure, edge systems, and secure operational environments. Models can be integrated into existing pipelines, dashboards, and alerting systems.

Live Video Integration

Real-time visual intelligence from operational video

IRIS integrates live video streams and recorded footage to enable real-time object detection and continuous system learning from operational data.

Platform Architecture

Built for scale, security, and flexibility

Flexible Data Ingestion

Connect visual data from any operational source

IRIS ingests video streams, image datasets, and sensor feeds from diverse environments. The platform standardizes incoming visual data to support scalable analysis and model development.

GPU-Optimized Processing

High-performance compute for visual AI workloads

IRIS leverages GPU-accelerated infrastructure to support large-scale model training, inference, and experiment workloads. Parallel processing enables rapid experimentation and iteration.

API & Integrations

Seamless integration with existing systems

IRIS provides robust API access for integration with existing data pipelines, monitoring platforms, and operational systems. This enables organizations to embed visual intelligence directly into their workflows.

Continuous Learning

Models that evolve with real-world data

IRIS incorporates feedback from validation and production environments to continuously refine datasets and improve model performance over time.

Service Plans

IRIS service plans provide the right level of expert support, services, and resources to suit your needs.

Model Development

Custom object detection models built on your data in weeks, not months

Fixed scope
Production-ready models
Multiple architectures evaluated

View on AWS Marketplace

Dataset Development & Annotation

Rapid dataset creation using IRIS-driven candidate discovery

Reduce manual labeling effort
High-quality annotation at scale
Built for real-world conditions

View on AWS Marketplace

Model Evaluation & Benchmarking

Understand what actually works before deployment

Compare any architecture
Failure mode analysis
Controlled comparisons

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Full System Integration

End-to-end deployment from sensor to decision

Edge or cloud deployment
Monitoring and retraining workflows
Custom integrations

View on AWS Marketplace

Ready to Get Started?

See how IRIS can transform your computer vision workflows

Platform Overview

The Complete Computer Vision Platform

From raw data to deployed models—IRIS provides everything you need to build, train, and operate computer vision systems at scale.

Explore Workflow

How IRIS Works

A continuous cycle of improvement—from raw data to production-ready models and back again

ContinuousIteration

Deploy improvements back into production

Connect

Visual Data Ingestion

Core Capabilities

Everything you need to build production-grade computer vision systems

Visual Data Labeling

Structured dataset creation for computer vision systems

Similarity-Based Discovery

Accelerated dataset expansion through visual pattern discovery

Architecture-Agnostic Training

Experiment across multiple detection approaches

Experiment Management

Full lifecycle experiment tracking

Flexible Deployment

Deploy models to operational environments

IRIS supports deployment across cloud infrastructure, edge systems, and secure operational environments. Models can be integrated into existing pipelines, dashboards, and alerting systems.

Live Video Integration

Real-time visual intelligence from operational video

IRIS integrates live video streams and recorded footage to enable real-time object detection and continuous system learning from operational data.

Platform Architecture

Built for scale, security, and flexibility

Flexible Data Ingestion

Connect visual data from any operational source

IRIS ingests video streams, image datasets, and sensor feeds from diverse environments. The platform standardizes incoming visual data to support scalable analysis and model development.

GPU-Optimized Processing

High-performance compute for visual AI workloads

IRIS leverages GPU-accelerated infrastructure to support large-scale model training, inference, and experiment workloads. Parallel processing enables rapid experimentation and iteration.